Metabolic Syndrome

Association of triglyceride-cholesterol-body weight index with in-hospital mortality in critically ill patients with heart failure.

Malnutrition and metabolic abnormalities are common in patients with heart failure (HF) requiring intensive care unit (ICU) admission and are associated with poor outcomes. This study evaluated the association between triglyceride-cholesterol-body weight index (TCBI) and in-hospital mortality in critically ill patients with HF and developed a machine learning-based model to assess its predictive v...

Deep chemical structure graph learning deciphers the lipotoxicity code of hypertriglyceridemic pancreatitis.

The discrepancy between serum triglyceride levels and the clinical severity of hyperlipidemic acute pancreatitis (HLAP) complicates risk stratification. Traditional lipidomics, which primarily rely on linear abundance, often fail to distinguish the HLAP-specific lipidome from the metabolic background of hypertriglyceridemia (HTG). To overcome this limitation, DeepLipiDecipher was developed as a kn...

Incorporating geospatial environmental exposure indicators in individual hypertension risk prediction: a multi-stage machine learning pipeline.

BACKGROUND: Environmental exposures are known contributors to chronic disease but are rarely incorporated into risk prediction models. OBJECTIVE: We d...

Identification and validation of dyslipidemia and glutamate metabolism-associated gene diagnostic and therapeutic repurposing signature for Acute myocardial infarction patients: insights from integrative bioinformatic and multi-omic studies.

Objective Dyslipidemia and glutamate metabolism (DG) plays a crucial role in the pathogenesis of Acute myocardial infarction (AMI). Hence, deeper unde...

Stress hyperglycemia ratio improves 28-day mortality prediction beyond GRACE score in critically ill patients with acute coronary syndrome: a retrospective cohort study from MIMIC-IV.

BACKGROUND: The stress hyperglycemia ratio (SHR) is increasingly recognized as a reliable biomarker for adverse outcomes. However, its prognostic valu...

Echocardiography-Based Machine Learning Model for Atrial Fibrillation Risk Assessment in Hypertension.

BACKGROUND: Early identification of atrial fibrillation (AF) allows for timely interventions to reduce cardiovascular complications. Risk scores inclu...

Targeting dysregulated glycolysis in type 2 diabetic osteoporosis: Identification of a diagnostic gene signature and therapeutic validation of calcifediol.

Type 2 diabetic osteoporosis (T2DOP) is a significant complication of type 2 diabetes, characterized by an increased risk of fractures. While dysregul...

Diagnostic value of machine-learning using conventional magnetic resonance imaging markers for pediatric idiopathic intracranial hypertension: a retrospective study.

BACKGROUND: Pediatric idiopathic intracranial hypertension can be challenging to diagnose; magnetic resonance imaging (MRI) signs are considered suppo...

Integrative multi-omics and machine learning reveal glycolysis-related biomarkers driving vascular remodeling in pulmonary arterial hypertension.

Metabolic reprogramming toward aerobic glycolysis, a phenomenon analogous to the Warburg effect, is increasingly recognized as a hallmark of pulmonary...

The role of nitrogen dioxide in the prevalence of adverse cardiovascular and cerebrovascular diseases in China: a national multi-pollutant geospatial analysis.

INTRODUCTION: Cardiovascular and cerebrovascular diseases (CCVDs) pose a severe global health threat, particularly among middle-aged and elderly popul...

Attitudes toward artificial intelligence tools in university students: associations with body appreciation and e-healthy diet literacy - implications for digital health education.

BACKGROUND: This cross-sectional study aimed to determine attitudes toward the use of artificial intelligence tools, body appreciation, and e-healthy ...

Combined effects of cumulative triglyceride-glucose and blood pressure on stroke in middle-aged and older Chinese adults: a longitudinal analysis.

BACKGROUND: The triglyceride-glucose (TyG) index has increasingly been recognised an indicator for stroke risk. We aimed to explore the relationship b...

AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.

Systemic vascular and neurodegenerative disorders are important causes of disability and death worldwide, mainly because of the late stage of diagnosi...

Uric Acid to HDL-C Ratio as a Novel Biomarker for Sarcopenia: A National Study with Machine Learning Insights.

BACKGROUND: Sarcopenia is a major age-related health burden. Although the uric acid to high-density lipoprotein cholesterol ratio (UHR) has been linke...

Estimated glucose disposal rate and triglyceride-glucose index jointly predict mortality in advanced cardiovascular-kidney-metabolic syndrome.

OBJECTIVE: Advanced cardiovascular-kidney-metabolic (CKM) syndrome carries substantial mortality risk, yet the independent and joint prognostic roles ...

Machine learning based hepatic safety score predicts decompensation in hepatocellular carcinoma systemic therapy.

Hepatocellular carcinoma (HCC) frequently coexists with portal hypertension, significantly increasing the risk of hepatic decompensation (HD) and vari...

Integrative transcriptomic analysis identifies immune-associated candidate genes and altered immune cell infiltration in pulmonary arterial hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a progressive vascular disease characterized by immune dysregulation and pulmonary vascular remod...

Machine Learning Applications Within the Earlier Medicine Framework for Stroke: A Scoping Review.

This scoping review explores how machine learning (ML) has been applied to stroke research within the Earlier Medicine framework, which promotes proac...

Integrating Multi-Source Biomedical Data for Adverse Drug Reaction Analysis: A Knowledge Graph Approach.

Understanding adverse drug reaction mechanisms requires integrating information from multiple heterogeneous sources. We developed a pipeline for combi...

Targeting metabolic dysregulation in postmenopausal breast cancer: Synergistic effects of nutrition-based interventions and exercise on molecular and cellular pathways.

Postmenopausal females diagnosed with breast cancer who are undergoing aromatase inhibitor (AI) therapy often exhibit metabolic disturbances, which ma...